However, all of these works are founded on conventional approaches including trial-and-error methods, brute force optimization methods, and parameter sweep, which are time-consuming processes. Therefore, to solve the above challenges and to find a fast, effective, and automated route for metasurface design, we have benefited from machine learning. Deep learning is an efficient approach for learning the relationship between input and target data from the samples of past experiences. To be more specific, deep learning as a specific branch of machine learning can infer the basic rules based on formerly specified data; then, for another given input, the designed network can make reasonable decisions. With the ever-increasing growth of machine learning and its potential applications to address some important problems such as signal processing[1]} and through the wall imaging[2]}, we are now witnessing the opening of machine learning in wave-interaction phenomena. Owing to its potential capacity to provide higher accuracy, less design time, and improve the efficiency of a design process, machine learning has been introduced in numerous electromagnetic phenomena, for instance, all-dielectric metasurfaces[3]}, antenna design[4]}, [5]}, acoustic metamaterials[6]}, [7]}, and computational electromagnetics[8]}, [9]}.
